### Communities and community detection

A *cluster algorithm for graphs* means exactly the same as a *community detection
algorithm for networks*, and *community structure* in networks means exactly
the same as *cluster structure* in graphs.
This is a severe and really unfortunate case of divergent terminology.
My training as a mathematician has led me to use *graph* predominantly.
This word has other meanings however, and is not always intuitive to people
from other realms of science. Hence I have begun to appreciate and increasingly use
*network*. On the other hand, the phrase *community detection*
seems rather narrow and I strongly prefer the older idioms *clustering* and *cluster analysis*.
Throughout these pages and the MCL documentation *graph* is used a lot,
nowadays interspersed with usage of *network*. These should
be understood to be entirely interchangeable — not just on these pages, but
in a very broad sense. Similarly, communities are the same as clusters
in the context of, well, graph clustering, also-known-as community detection
in networks.

### Partitions and graph partitioning

The concept of *partition*
or *partitioning* means superficially the same as *clustering*, that is, a
separation into mutually disjoint subsets that cover the entire set of
interest.

The most important difference is that the problem of *graph partitioning*
is universally defined as a problem where *the number and sizes of the clusters
are specified a priori*. This is not the case in *graph clustering* or
*cluster analysis* in general.
The second, less important difference between these two terms is that *clustering*
excludes the possibility of overlap by convention, so that it is still
possible to speak of an *overlapping clustering*, whereas a
*partition* or *partitioning* excludes the possibility of overlap by
definition.